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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- emotion |
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metrics: |
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- accuracy |
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model-index: |
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- name: MiniLMv2-L6-H384-emotion |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: emotion |
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type: emotion |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9215 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# MiniLMv2-L6-H384-emotion |
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This model is a fine-tuned version of [nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large](https://huggingface.co/nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large) on the emotion dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2140 |
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- Accuracy: 0.9215 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 8 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 1.432 | 1.0 | 500 | 0.9992 | 0.6805 | |
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| 0.8073 | 2.0 | 1000 | 0.5437 | 0.846 | |
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| 0.4483 | 3.0 | 1500 | 0.3018 | 0.909 | |
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| 0.2833 | 4.0 | 2000 | 0.2412 | 0.915 | |
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| 0.2169 | 5.0 | 2500 | 0.2140 | 0.9215 | |
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| 0.1821 | 6.0 | 3000 | 0.2159 | 0.917 | |
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| 0.154 | 7.0 | 3500 | 0.2084 | 0.919 | |
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| 0.1461 | 8.0 | 4000 | 0.2047 | 0.92 | |
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### Framework versions |
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- Transformers 4.12.3 |
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- Pytorch 1.9.1 |
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- Datasets 1.15.1 |
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- Tokenizers 0.10.3 |
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